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Jinshi Cui

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15 papers

2 author rows

IJCAI Conference 2024 Conference Paper

AllMatch: Exploiting All Unlabeled Data for Semi-Supervised Learning

Zhiyu Wu
Jinshi Cui

Existing semi-supervised learning algorithms adopt pseudo-labeling and consistency regulation techniques to introduce supervision signals for unlabeled samples. To overcome the inherent limitation of threshold-based pseudo-labeling, prior studies have attempted to align the confidence threshold with the evolving learning status of the model, which is estimated through the predictions made on the unlabeled data. In this paper, we further reveal that classifier weights can reflect the differentiated learning status across categories and consequently propose a class-specific adaptive threshold mechanism. Additionally, considering that even the optimal threshold scheme cannot resolve the problem of discarding unlabeled samples, a binary classification consistency regulation approach is designed to distinguish candidate classes from negative options for all unlabeled samples. By combining the above strategies, we present a novel SSL algorithm named AllMatch, which achieves improved pseudo-label accuracy and a 100% utilization ratio for the unlabeled data. We extensively evaluate our approach on multiple benchmarks, encompassing both balanced and imbalanced settings. The results demonstrate that AllMatch consistently outperforms existing state-of-the-art methods.

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AAAI Conference 2023 Conference Paper

BERT-ERC: Fine-Tuning BERT Is Enough for Emotion Recognition in Conversation

Xiangyu Qin
Zhiyu Wu
Tingting Zhang
Yanran Li
Jian Luan
Bin Wang
Li Wang
Jinshi Cui

Previous works on emotion recognition in conversation (ERC) follow a two-step paradigm, which can be summarized as first producing context-independent features via fine-tuning pretrained language models (PLMs) and then analyzing contextual information and dialogue structure information among the extracted features. However, we discover that this paradigm has several limitations. Accordingly, we propose a novel paradigm, i.e., exploring contextual information and dialogue structure information in the fine-tuning step, and adapting the PLM to the ERC task in terms of input text, classification structure, and training strategy. Furthermore, we develop our model BERT-ERC according to the proposed paradigm, which improves ERC performance in three aspects, namely suggestive text, fine-grained classification module, and two-stage training. Compared to existing methods, BERT-ERC achieves substantial improvement on four datasets, indicating its effectiveness and generalization capability. Besides, we also set up the limited resources scenario and the online prediction scenario to approximate real-world scenarios. Extensive experiments demonstrate that the proposed paradigm significantly outperforms the previous one and can be adapted to various scenes.

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NeurIPS Conference 2021 Conference Paper

Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning

Hanzhe Hu
Fangyun Wei
Han Hu
Qiwei Ye
Jinshi Cui
Liwei Wang

Due to the limited and even imbalanced data, semi-supervised semantic segmentation tends to have poor performance on some certain categories, e. g. , tailed categories in Cityscapes dataset which exhibits a long-tailed label distribution. Existing approaches almost all neglect this problem, and treat categories equally. Some popular approaches such as consistency regularization or pseudo-labeling may even harm the learning of under-performing categories, that the predictions or pseudo labels of these categories could be too inaccurate to guide the learning on the unlabeled data. In this paper, we look into this problem, and propose a novel framework for semi-supervised semantic segmentation, named adaptive equalization learning (AEL). AEL adaptively balances the training of well and badly performed categories, with a confidence bank to dynamically track category-wise performance during training. The confidence bank is leveraged as an indicator to tilt training towards under-performing categories, instantiated in three strategies: 1) adaptive Copy-Paste and CutMix data augmentation approaches which give more chance for under-performing categories to be copied or cut; 2) an adaptive data sampling approach to encourage pixels from under-performing category to be sampled; 3) a simple yet effective re-weighting method to alleviate the training noise raised by pseudo-labeling. Experimentally, AEL outperforms the state-of-the-art methods by a large margin on the Cityscapes and Pascal VOC benchmarks under various data partition protocols. Code is available at https: //github. com/hzhupku/SemiSeg-AEL.

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ICRA Conference 2017 Conference Paper

Ego-centric traffic behavior understanding through multi-level vehicle trajectory analysis

Donghao Xu
Xu He
Huijing Zhao
Jinshi Cui
Hongbin Zha
Franck Guillemard
Stéphane Géronimi
François Aioun

This study proposes a multi-level trajectory analysis method for modeling traffic behavior from an ego-centric view, where on-road vehicle trajectories are collected based on the authors' previous studies of an on-board system consisting of multiple 2D lidar sensors. From an input set of trajectories, a set of hot regions (topics) that trajectory points most frequently present are first discovered using a sticky HDP-HMM; then, the major paths of the trajectories' transitions across different hot regions are extracted by recursively mining frequent subsequences of topics; and finally, paths are modeled using a hierarchical hidden Markov model (HHMM), where the intra-path dynamics is represented using an HMM, in which each state corresponds to a hot region, while the inter-path transition is assumed to be Markovian. The model could be used for behavior prediction, i. e. whenever a vehicle is detected in a scene, predicting which route it will probably follow and how its trajectory will probably develop over time, which is essential to interpreting the potential risks for longer time horizons. Experiments are conducted using a large set of vehicle trajectories collected from motorways in Beijing, and promising results are presented.

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ICRA Conference 2014 Conference Paper

Calibration method for multiple 2D LIDARs system

Mengwen He
Huijing Zhao
Jinshi Cui
Hongbin Zha

Many robotic and mobile mapping systems have been developed using multiple 2D LIDARs (briefly multi-LIDAR system) to sense environment. In such systems, extrinsic calibration of all LIDARs is essential for making collaborative use of the data from different sensors. This research aims at developing a calibration method for multi-LIDAR systems at the general scene, such as an outdoor place or an underground parking-lot, without modification to environment by putting calibration targets. In this paper, the calibration method is proposed by aligning the 3D data of different LIDARs. They are concerned at two-levels: 1) reference calibration, i. e. finding the transformation from a reference LIDAR to the platform frame; 2) multi-LIDAR calibration, i. e. finding the LIDARs' relative geometries by referring to the reference one. The method is examined in calibrating the multiple 2D LIDARs on an intelligent vehicle platform POSS-V, where the data collected through a driving in an underground parking-lot are registered to find sensors' geometry. Calibration accuracy is examined by comparing with a CAD model of the scene, which was measured by using a total station.

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TIST Journal 2013 Journal Article

A fully online and unsupervised system for large and high-density area surveillance

Xuan Song
Xiaowei Shao
Quanshi Zhang
Ryosuke Shibasaki
Huijing Zhao
Jinshi Cui
Hongbin Zha

For reasons of public security, an intelligent surveillance system that can cover a large, crowded public area has become an urgent need. In this article, we propose a novel laser-based system that can simultaneously perform tracking, semantic scene learning, and abnormality detection in a fully online and unsupervised way. Furthermore, these three tasks cooperate with each other in one framework to improve their respective performances. The proposed system has the following key advantages over previous ones: (1) It can cover quite a large area (more than 60×35m), and simultaneously perform robust tracking, semantic scene learning, and abnormality detection in a high-density situation. (2) The overall system can vary with time, incrementally learn the structure of the scene, and perform fully online abnormal activity detection and tracking. This feature makes our system suitable for real-time applications. (3) The surveillance tasks are carried out in a fully unsupervised manner, so that there is no need for manual labeling and the construction of huge training datasets. We successfully apply the proposed system to the JR subway station in Tokyo, and demonstrate that it can cover an area of 60×35m, robustly track more than 150 targets at the same time, and simultaneously perform online semantic scene learning and abnormality detection with no human intervention.

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TIST Journal 2013 Journal Article

An online system for multiple interacting targets tracking

Xuan Song
Huijing Zhao
Jinshi Cui
Xiaowei Shao
Ryosuke Shibasaki
Hongbin Zha

Multitarget tracking becomes significantly more challenging when the targets are in close proximity or frequently interact with each other. This article presents a promising online system to deal with these problems. The novelty of this system is that laser and vision are integrated with tracking and online learning to complement each other in one framework: when the targets do not interact with each other, the laser-based independent trackers are employed and the visual information is extracted simultaneously to train some classifiers online for “possible interacting targets”. When the targets are in close proximity, the classifiers learned online are used alongside visual information to assist in tracking. Therefore, this mode of cooperation not only deals with various tough problems encountered in tracking, but also ensures that the entire process can be completely online and automatic. Experimental results demonstrate that laser and vision fully display their respective advantages in our system, and it is easy for us to obtain a good trade-off between tracking accuracy and the time-cost factor.

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IROS Conference 2013 Conference Paper

Pairwise LIDAR calibration using multi-type 3D geometric features in natural scene

Mengwen He
Huijing Zhao
Franck Davoine
Jinshi Cui
Hongbin Zha

It has become a well-known technology that 3D measurement of a large environment could be achieved by using a number of 2D LIDARs on a mobile platform. In such a system, calibration is essential for making collaborative use of different LIDAR data, while existing methods usually require modifications to the environments, such as putting calibration targets, or rely on special facilities, which is labor intensive and put many restrictions to potential applications. This research aims at developing a calibration method for multiple 2D LIDAR sensing systems, which could be conducted in a general outdoor environment using the features of a nature scene. Special focus is cast on solving the noisy sensing in a complex environment and the occlusions caused by largely different sensor viewpoints. A multi-type geometric feature based calibration algorithm is proposed, which extracts the features such as points, lines, planes and quadrics from the 3D points of each LIDAR sensing. Transformation parameters from each sensor to the frame on a moving platform is estimated by matching the multi-type features. Experiments are conducted using the data sets of an intelligent vehicle platform (POSS-V) through a driving in the campus of Peking University. Results of calibrating two LIDAR sensors with largely different viewpoints are presented, and the accuracy and robustness concerning noisy feature extractions are examined intensively.

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ICRA Conference 2011 Conference Paper

A novel laser-based system: Fully online detection of abnormal activity via an unsupervised method

Xuan Song 0001
Xiaowei Shao
Ryosuke Shibasaki
Huijing Zhao
Jinshi Cui
Hongbin Zha

Abnormal activity detection plays a crucial role in surveillance applications, and such system has become an urgent need for public security. In this paper, we propose a novel laser-based system, which can perform the online detection of abnormal activity with an unsupervised way. The proposed system has the following key features that make it advantageous over previous ones: (1) It can cover quite a large and crowded area, such as subway station, public square, intersection and etc. (2) The overall system can vary with time period, incrementally learn the behavior pattern of pedestrians and perform the fully online detection of abnormal activity. This feature makes our system be quite suitable for the real-time applications. (3) The abnormal activity detection is carried out with a fully unsupervised way, there is no need for manual labelling and constructing the huge training datasets. We successfully applied the proposed system into the JR subway station of Tokyo, which can cover a 60×35m area, track more 150 targets at the same time and simultaneously perform the robust detection of abnormal activity with no human intervention.

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ICRA Conference 2010 Conference Paper

Fusion of laser and vision for multiple targets tracking via on-line learning

Xuan Song 0001
Huijing Zhao
Jinshi Cui
Xiaowei Shao
Ryosuke Shibasaki
Hongbin Zha

Multi-target tracking becomes significantly more challenging when the targets are in close proximity or frequently interact with each other. This paper presents a promising tracking system to deal with these problems. The novelty of this system is that laser and vision, tracking and learning are integrated and can complement each other in one framework: when the targets do not interact with each other, the laser-based independent trackers are employed and the visual information is extracted simultaneously to train some classifiers for the “possible interacting targets”. When the targets are in close proximity, the learned classifiers and visual information are used to assist in tracking. Therefore, this mode of co-operation between them not only deals with various tough problems encountered in the tracking, but also ensures that the entire process can be completely on-line and automatic. Experimental results demonstrated that laser and vision fully display their respective advantages in our system, and it is easy for us to obtain a perfect trade-off between tracking accuracy and time-cost.

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ICRA Conference 2009 Conference Paper

Moving object classification using horizontal laser scan data

Huijing Zhao
Quanshi Zhang
Masaki Chiba
Ryosuke Shibasaki
Jinshi Cui
Hongbin Zha

Motivated by two potential applications, i. e. enhancing driving safety and traffic data collection, a system has been developed using a single-layer horizontal laser scanner as the major sensor for both localization and perception of the surroundings in a large dynamic urban environment. This research focuses on a classification method, that given a stream of laser measurements, classify the moving object into either a person, a group of people, a bicycle or a car. In this research, a number of features are defined after examining the property of data appearance. A classification method is proposed after examining the likelihood measures between each pair of feature and class. Experimental results are presented, demonstrating that the algorithm has efficiency with respect to both driving safety and traffic data collection in highly dynamic environment.

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ICRA Conference 2008 Conference Paper

SLAM in a dynamic large outdoor environment using a laser scanner

Huijing Zhao
Masaki Chiba
Ryosuke Shibasaki
Xiaowei Shao
Jinshi Cui
Hongbin Zha

In this research, we propose a method of SLAM in a dynamic large outdoor environment using a laser scanner. Focus are cast on solving two major problems: 1) achieving global accuracy especially in non-cyclical environment, 2) tackling a mixture of data from both dynamic and static objects. Algorithms are developed, where GPS data and control inputs are used to diagnose pose error and guide to achieve a global accuracy; Classification of laser points and objects are conducted not in an independent module but across the processing in a framework of SLAM with moving object detection and tracking. Experiments are conducted using the data from two test-bed vehicles, and performance of the algorithms are demonstrated.

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ICRA Conference 2008 Conference Paper

Tracking interacting targets with laser scanner via on-line supervised learning

Xuan Song 0001
Jinshi Cui
Xulei Wang
Huijing Zhao
Hongbin Zha

Successful multi-target tracking requires locating the targets and labeling their identities. For the laser based tracking system, the latter becomes significantly more challenging when the targets frequently interact with each other. This paper presents a novel on-line supervised learning based method for tracking interacting targets with laser scanner. When the targets do not interact with each other, we collect samples and train a classifier for each target. When the targets are in close proximity, we use these classifiers to assist in tracking. Different evaluations demonstrate that this method has a better tracking performance than previous methods when interactions occur, and can maintain correct tracking under various complex tracking situations.

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IROS Conference 2006 Conference Paper

Laser-based Interacting People Tracking Using Multi-level Observations

Jinshi Cui
Hongbin Zha
Huijing Zhao
Ryosuke Shibasaki

Laser based people tracking systems have been developed for mobile robotics and intelligent surveillance areas. Existing systems rely on simple laser point clustering methods to extract object locations. However, when dealing with multiple interacting people, laser points of different persons are often interlaced and undistinguishable due to measurement noise and they can not provide reliable features. It causes current systems quite fragile and unreliable. In this paper, we try to explore potentials from multi-level observations including weakly detected features, stably extracted features and foreground points. For inference, detection incorporated joint particle filter is used. And stably extracted features are utilized to properly estimate parameters of dynamic model for each target. In real experiments, we obtain raw data from multiple registered laser scanners, which measure two legs for each people. Evaluations with real data show that the proposed method is more robust and effective than existing approaches

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IROS Conference 2005 Conference Paper

Tracking multiple people using laser and vision

Jinshi Cui
Hongbin Zha
Huijing Zhao
Ryosuke Shibasaki

We present a novel system that aims at reliably detecting and tracking multiple people in an open area. Multiple single-row laser scanners and one video camera are utilized. Feet trajectory tracking based on registration of distance information from multiple laser scanners and visual body region tracking based on color histogram are combined in a Bayesian formulation. Results from tests in a real environment are reported to demonstrate that the system can detect and track multiple people simultaneously with reliable and real-time performance.

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